Demands on availability and reliability of vacuum pumps in modern semiconductor manufacturing processes have been constantly increasing. It is the reason that the costs for failed wafer batches and lost production times are higher and higher as the size of the production wafer is larger and larger. Technical demands on the vacuum pumps for such modern semiconductor processes have been well pointed out by Bahnen and Kuhn [Reference 1: R. Bahnen and M Kuhn, “Increased reliability of dry pumps due to process related adaptation and pre-failure warning,” Vacuum, Vol. 44, No 5-7, pp. 709-712, 1993]: High reliability without unscheduled downtime, very low maintenance, high capability of pumping corrosive and reactive gas mixtures, high capability of pumping particles and sublimable gas mixtures, and low vibration and noise levels, etc. In order to satisfy those demands, a new dry pump for the modern semiconductor processes should provide not only the adaptation capability for the various process-dependent running conditions but also the monitoring schemes of the pump operation-related parameters (electrical power, cooling water, purge gas, wear of pump parts—bearings, seals, gear box, and motor) to avoid the risk of unscheduled downtime. Bahnen and Kuhn [Ref. 1] suggested the warning and alarm level-based monitoring scheme for the process-related or operation-related parameters to avoid unexpected pump failures. But, any logical way of selecting all the warning and alarming levels of process-dependent and operation-related parameters are not proposed. Such threshold level selection is still a very challenging issue in the early detection of vacuum pump failure. Moreover, the proposed monitoring scheme does not provide any technical way of determining an appropriate replacement time of a vacuum pump issuing warning or alarm signals. It has been also another challenging issue to vacuum pump maintenance engineers. It is the reason that either warning or alarm is not a direct indicator for the pump replacement and that pump maintenance engineers can determine pump replacement only after examining sufficient technical information about the evaluated vacuum pump performance and monitored running conditions. This work will be shown not only to present a systematic way of accessing the quantitative degradation of vacuum pumps that have issued the warning or alarm signals but also to enable pump maintenance engineers to determine pump replacement on the basis of the performance assessment results.
The threshold level-based monitoring has been widely recognized as a traditional technique for the failure protection of pumps [Reference 2: R. H. Greene and D. A. Casada, Detection of pump degradation, NUREG/CR-6089/ORNL-6765, Oak Ridge National Laboratory, 1995]. Wegerich et al [Reference 3: S. W. Wegerich, D. R. Bell and X. Xu, “Adaptive modeling of changed states in predictive condition monitoring,” WO 02/057856 A2, 2002; Reference 4: S. W. Wegerich, A. Wolosewicz and R. M Pipke, “Diagnostic systems and methods for predictive condition monitoring,” WO 02/086726 A1, 2002], however, pointed out the drawbacks of the sensor output-based threshold warning and alarming schemes: “The traditional technique could not provide responses to gross changes in operational parameters of a process or machine, often failed to provide adequate warning to prevent unexpected shutdowns, equipment damage or catastrophic safety hazards.” In order to overcome such limit of the traditional technique, they suggested the use of the neural network-based parametric model adaptive to new operational states [Ref. 3] and the model-based diagnostic systems for predictive condition monitoring [Ref. 4]. The neural network model, as known in the previous study [Reference 5: Wan-Sup Cheung, “Identification, stabilization and control of nonlinear systems using the neural network-based parametric nonlinear modelling,” Ph.D. Thesis, University of Southampton, 1993] on the identification and control of dynamic systems, has the useful capability of interpolating a new state lying between trained data sets and extrapolating a neighboring state outside (but very near) the trained sets. Wegerich et al [Ref. 3, Ref. 4] exploited the interpolation and extrapolation capability [Ref. 5] of the trained neural network to estimate the current state of the process or machine in response to the measured values of sensor outputs. The residuals between the estimated state values and the measured ones are also used to generate the residual threshold alert, to perform the statistical test to check the shift of the process or system to a new operation condition, and to rebuild up a new training set for the shifted operation region. The suggested signal processing schemes of generating the alerts and adapting to the shifted operation region, including the construction of the new training set for the shifted operation region and their model learning process, are seen not only to require severe computation work but also to accompany the inherent complexity of the suggested model-based diagnostic system. Such unrealistic computation load and implementation complexity of the suggested monitoring system has became unavoidable technical issues encountered in the pump monitoring and diagnostic systems for the modern semiconductor manufacturing processes. Moreover, the suggested model-based diagnostic system does not provide any systematic way of evaluating of the performance of the vacuum pumps running under the varied operation conditions. Consequently, these technical issues have been the main motivation of this invention to develop not only a simple model adaptive to the pump operation conditions but also the new evaluation schemes of the vacuum pump performance indicators applicable to the pump-installed sites. This work proposes the predictive maintenance scheme of vacuum pumps, which always estimates the pump performance indicators whenever warning or alarm signals are observed. This direct performance evaluation scheme needs neither training sets nor trained models suggested by Wegerich et al [Ref. 3, Ref. 4].
Instead of using the above parametric models adaptive to varying operation conditions of vacuum pumps with age, Ushiku et al [Reference 6: Y. Ushiku, T. Arikado, S. Samata, T. Nakao, and Y. Mikata, “Apparatus for predicting life of rotary machine, equipment using the same, method for predicting life and determining repair timing of the same,” U.S. Patent Application Publication, US2003/0009311 A1, 2003], Samata et al [Reference 7: S. Samata, Y. Ushiku, K. Ishii, and T. Nakao, “Method for diagnosing life of manufacturing equipment using rotary machine,” U.S. Patent Application Publication, US2003/0154052 A1, 2003; Reference 8: S. Samata, Y. Ushiku, T. Huruhata, T. Nakao, and K. Ishii, “Method for predicting life span of rotary machine used in manufacturing apparatus and life predicting system,” U.S. Patent Application Publication, US2003/01543997 A1, 2003] and Ishii et al [Reference 9: K. Ishii, T. Nakao, Y. Ushiku, and S. Samata, “Method for avoiding irregular shutoff of production equipment and system for irregular shutoff,” U.S. Patent Application Publication, US2003/0158705 A1, 2003] suggested the statistical analysis methods and the Mahalanobis distance-based analysis method [Reference 10: W. H. Woodall, R. Koudelik, Z. G. Stoumbos, K. L. Tsui, S. B. Kim, C P. Carvounis, “A review and analysis of the Mahalanobis-Taguchi system,” TECHNOMETRICS, Vol. 45, No. 1, pp. 1-14, 2003] to determine whether or not the currently measured time series data are deviated from the “reference” time series data set corresponding to the normal operation conditions. The statistical analysis methods are based on the second order statistical properties of sampled signals [Reference 11: J. S. Bendat A. G. Piersol, Random data: Analysis and measurement procedures, John Wiley & Sons: N.Y., 1985], such as the averaged values, standard deviations, and correlation functions. Because the use of the statistical properties makes sense only to the stationary processes, they have limited applicability to multiple load-dependent operation conditions required for the different products. It means that each reference time series data set corresponding to each load-dependent operation is required. A critical issue here is how to construct the data sets of load-dependent reference time series sufficient to cover the full range of normal operation conditions. Any effective way for constructing them is not yet proposed by Y. Ushiku et al [Ref. 6], Samata et al [Ref. 7, Ref. 8] and Ishii et al [Ref. 9]. Although the time series of normal operation conditions for new or reconditioned vacuum pumps are available only at the very beginning of each designated process, the reference data with the full range of normal operation conditions could be not obtained without the time-consuming data acquisition and signal processing jobs. In reality, a modern semiconductor fabrication unit requires multiple processes with such different operation conditions as varying camber pressures, gas flow rates, and different gas mixtures and properties. Those process-related properties and operation conditions of semiconductor manufacturers are very confidential such that they are very often inaccessible to the vacuum pump suppliers. It is very significant to note that a vacuum pump monitoring and diagnosis system for the modern semiconductor processes should have the capacity of self-adapting to multiple process conditions. It should be noted that since the proposed statistical analysis methods [Ref. 6-Ref. 9] do not consider any systematic basis of evaluating the vacuum pump performance indicators they can not provide any quantitative data of pump degradation that enables pump maintenance engineers to determine an suitable time for pump replacement. This invention will be shown to provide a realistic solution to solve such technical issues later, without using the reference data with the wide range of normal operation conditions collected in the previous work [Ref. 6-Ref. 9].